Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern Italy
A great deal of attention has been devoted to the analysis of particulate matter (PM) concentrations in various scenarios because of their negative effects on human health. Here, we investigate how meteorological conditions can affect PM concentrations in the peculiar case of the district of the cit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/3/475 |
| _version_ | 1797613543228440576 |
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| author | Andrea Tateo Vincenzo Campanaro Nicola Amoroso Loredana Bellantuono Alfonso Monaco Ester Pantaleo Rosaria Rinaldi Tommaso Maggipinto |
| author_facet | Andrea Tateo Vincenzo Campanaro Nicola Amoroso Loredana Bellantuono Alfonso Monaco Ester Pantaleo Rosaria Rinaldi Tommaso Maggipinto |
| author_sort | Andrea Tateo |
| collection | DOAJ |
| description | A great deal of attention has been devoted to the analysis of particulate matter (PM) concentrations in various scenarios because of their negative effects on human health. Here, we investigate how meteorological conditions can affect PM concentrations in the peculiar case of the district of the city of Lecce in the Apulia region (Southern Italy), which is characterized by the highest tumor rate of the whole region despite the absence of nearby heavy industries. We present a unified machine learning framework which combines air quality and meteorological data, either measured on ground or forecast. Our findings show that the concentrations of <i>PM</i><sub>10</sub>, <i>PM</i><sub>2.5</sub>, <i>NO</i><sub>2</sub> and <i>CO</i> are significantly associated with the meteorological conditions and suggest that it is possible to predict air quality using either ground weather observations or weather forecasts. |
| first_indexed | 2024-03-11T06:56:15Z |
| format | Article |
| id | doaj.art-22620269d89844879c89d09d032f61e4 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T06:56:15Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-22620269d89844879c89d09d032f61e42023-11-17T09:32:13ZengMDPI AGAtmosphere2073-44332023-02-0114347510.3390/atmos14030475Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern ItalyAndrea Tateo0Vincenzo Campanaro1Nicola Amoroso2Loredana Bellantuono3Alfonso Monaco4Ester Pantaleo5Rosaria Rinaldi6Tommaso Maggipinto7Apulia Region Environmental Protection Agency (ARPA Puglia), C.so Trieste 27, 70126 Bari, ItalyApulia Region Environmental Protection Agency (ARPA Puglia), C.so Trieste 27, 70126 Bari, ItalyDipartimento di Farmacia—Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, ItalyDepartment of Mathematics and Physics E. De Giorgi, Universitá del Salento, Via Arnesano, 73100 Lecce, ItalyIstituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, ItalyA great deal of attention has been devoted to the analysis of particulate matter (PM) concentrations in various scenarios because of their negative effects on human health. Here, we investigate how meteorological conditions can affect PM concentrations in the peculiar case of the district of the city of Lecce in the Apulia region (Southern Italy), which is characterized by the highest tumor rate of the whole region despite the absence of nearby heavy industries. We present a unified machine learning framework which combines air quality and meteorological data, either measured on ground or forecast. Our findings show that the concentrations of <i>PM</i><sub>10</sub>, <i>PM</i><sub>2.5</sub>, <i>NO</i><sub>2</sub> and <i>CO</i> are significantly associated with the meteorological conditions and suggest that it is possible to predict air quality using either ground weather observations or weather forecasts.https://www.mdpi.com/2073-4433/14/3/475meteorological conditionsair qualitytumor death ratemachine learningparticulate matter |
| spellingShingle | Andrea Tateo Vincenzo Campanaro Nicola Amoroso Loredana Bellantuono Alfonso Monaco Ester Pantaleo Rosaria Rinaldi Tommaso Maggipinto Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern Italy Atmosphere meteorological conditions air quality tumor death rate machine learning particulate matter |
| title | Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern Italy |
| title_full | Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern Italy |
| title_fullStr | Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern Italy |
| title_full_unstemmed | Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern Italy |
| title_short | Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern Italy |
| title_sort | predicting air quality from measured and forecast meteorological data a case study in southern italy |
| topic | meteorological conditions air quality tumor death rate machine learning particulate matter |
| url | https://www.mdpi.com/2073-4433/14/3/475 |
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